Abstract

We present an automated approach for cost model discovery in configuration spaces. Given a configuration space, a quality assurance (QA) task of interest, and a means of measuring the cost of carrying out the QA task, the proposed approach systematically sample the configuration space by using a traditional covering array, carry out the QA task in each of the selected configurations, measure the costs, and fit a generalized linear regression model to the observed costs. The resulting model is then used to estimate the cost of performing the QA task in a possibly previously unseen configuration. The results of our empirical studies conducted on two highly configurable and widely used software systems, strongly support our basic hypothesis that the proposed approach can efficiently and effectively discover reliable cost models.